A METHODOLOGY FOR CAVE FLOOR BASEMAP SYNTHESIS FROM POINT CLOUD DATA: A CASE STUDY OF SLAM-BASED LIDAR AT LAS CUEVAS, BELIZE
Bibliographic record
Abstract
Abstract. Creating cave maps is an essential part of cave research. Traditional cartographic efforts are extremely time consuming and subjective, motivating the development of new techniques using terrestrial lidar scanners and mobile lidar systems. However, processing the large point clouds from these scanners to produce detailed, yet manageable “maps” remains a challenge. In this work, we present a methodology for synthesizing a basemap representing the cave floor from large scale point clouds, based on a case study of a SLAM-based lidar data acquisition from a cave system in the archaeological site of Las Cuevas, Belize. In 4 days of fieldwork, the 335 m length of the cave system was scanned, resulting in a point cloud of 4.1 billion points, with 1.6 billion points classified as part of the cave floor. This point cloud was processed to produce a basemap that can be used in GIS, where natural and anthropogenic features are clearly visible and can be traced to create accurate 2D maps similar to traditional cartography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".